Copy Number Variations (CNVs) are a common cause of disease in humans. As of today, CNVs are most often detected using microarrays. However, microarrays are expensive and are not able to effectively detect CNVs below 1K bp range. This project is designed to detect both point mutations and CNVs in one clinical test without having to utilize microarrays. It is our goal to develop an easy to use solution that allows clinicians and researchers to conduct this type of advanced analysis without requiring bioinformatics and scripting skills. The resulting benefits include: ? Cost savings: By eliminating the need for additional microarray tests, labs will be able to streamline their analytics workflows utilizing NextGen Sequencing (NGS) data. ? Clinical yield: The proposed solution will be able to detect smaller CNV events in the sub 1K bp range that remain undetected by microarrays. This is crucial for clinicians as they are evaluating a genome for diagnostic purposes. ? Ease of Use: The proposed solution will be embedded in the Golden Helix VarSeq product that is designed to enable complex analytics workflows without the need to script or program. The simplification of advanced workflows such as the CNV analysis is crucial as precision medicine is becoming more and more mainstream. The simplicity of the solution will also streamline the training of healthcare professionals who are entering into this field. For our purpose, we may define CNVs as any deletion or insertion of DNA with respect to the human reference sequence of size ? 50 bps. Deletions and insertions shorter than 50 bp are common, but in general can be detected through routine variant calling algorithms used in the analysis of NGS data. CNVs may range in size up to several megabases. We are equally interested in detecting CNVs that are tens of kilobases or greater in size, as we are keen to extend the detection range as large as possible. CNV detection from NGS data is currently a key topic in human genetics. Different solutions from mostly research oriented groups have been developed. We will build upon the best of the solutions that have been described to date and make them commercially available. Most current solutions use models that are only capable of incorporating a single evidence metric. Our approach to CNV detection will instead make use of a probabilistic model capable of incorporating multiple evidence metrics derived from a collection of reference samples. This is a key improvement that differentiates our approach from existing methods described in the literature. Also, this work will lead to more advanced capabilities such as the detection of loss of heterozygosity (LOH), copy-neutral LOH and uniparental disomy (UPD) events.